Thursday, July 30, 2015

Aggregating Data to Prove an Adverse Impact Claim

Lopez v City of Lawrence is a case arising out of the United States District Court in Massachusetts. Plaintiffs, candidates for police Sergeant in a number of Massachusetts cities, including Boston, alleged that the Sergeant promotional examination had an adverse impact on the Plaintiffs.  The Plaintiffs were self described as African American or Hispanic. All of the promotional examinations were administered by the Human Resources Division, an agency of the Commonwealth of Massachusetts.  The examinations were given over a period of years from 2005 through 2008.  The promotional test consisted of 80 multiple choice written questions and 20 points based on Experience and Education.  A passing score of 70 was required to be placed on the promotional list.  Candidates were listed in order of scores with bonus points for Veterans and long service employees. 

The Plaintiffs sought to aggregate data from multiple municipalities and from multiple testing years to show a disparate impact on minorities. The Plaintiffs sought to aggregate data because the data for any one test or jurisdiction was too small to do a proper statistical analysis.  Experts testified that samples with fewer than 400 individuals can create unstable adverse impact ratios.  Most of the municipal examination pools involved approximately 40 to 50 candidates for promotion.

The District Court rejected the Plaintiffs’ argument and held that each test should be evaluated in relation to each municipality and for each testing period.  The Court said aggregating data from multiple jurisdictions is irrelevant and has the potential of producing testing anomalies.

In rejecting Plaintiffs’ aggregation argument the court said that each jurisdiction can only promote its own officers therefore it is irrelevant to use a labor pool from another jurisdiction.  Each city has its own limited pool from which to select candidates and these pools may be different due to variations in the original selection process, and training. With regard to aggregating data over multiple testing periods, the court said this is unreliable because repeat test takers could potentially distort the results.

Employers should watch this case as a reversal by the First Circuit Court of Appeals may make it easier for Plaintiffs to prove adverse impact where multiple candidate pools from different jurisdictions are compared across multiple testing dates.  

The municipal position in this case was supported in an amicus brief filed by the International Municipal Lawyers Association.